import tensorflow.keras as keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

batch_size = 128
num_classes = 10
epochs = 12


# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train,y_train), (x_test,y_test) = mnist.load_data()

if keras.backend.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0],1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# convert class vectors to binary class martrices
y_train = keras.utils.to_categorical(y_train,num_classes)
y_test = keras.utils.to_categorical(y_test,num_classes)

# model
model = keras.Sequential()
model.add(layers.Conv2D(32,
                        kernel_size=(3,3),
                        activation='relu',
                        input_shape=input_shape))
model.add(layers.Conv2D(64,
                        (3, 3),
                        activation="relu"))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Dropout(0.25))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer='adam',
              metrics=['accuracy'])

model.fit(x_train,y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test,y_test))

score = model.evaluate(x_test, y_test, verbose=0)

print(score)


